Optimization and Prediction of Mass Loss During Adhesive Wear of Nitrided AISI 4140 Steel Parts

Adhesive wear leads to substantial material loss, presenting a significant challenge across various industries. To address this issue, it is crucial to conduct studies aimed at mitigating this degradation. This particular study focuses on achieving a high-quality product with minimal mass loss during adhesive wear by utilizing gas nitriding treatment to optimize the wear parameters of 42CrMo4 steel. The study employed the Taguchi methodology and response surface methodology (RSM) to design the experiments. Key wear parameters, including wear speed (V), normal load (FN), and the microhardness of nitrided parts (HV), were thoroughly investigated. Additionally, an artificial neural network (ANN) prediction model was developed to forecast the wear performance of 4140 Steel. The ANN model demonstrated an accuracy of approximately 99% when compared to the experimental data. To further enhance the precision of wear estimation, prediction optimization was conducted using Bayesian and genetic algorithms. The results showed that the predicted R² values aligned reasonably well with the adjusted R² values, with a difference of less than 0.2. The analysis revealed that the normal load is the most critical factor influencing wear, followed by hardness, while wear speed has the least significant impact.